Geometry-based flight control system

ABSTRACT

A geometry-based flight control system is disclosed. In various embodiments, a set of inceptor inputs associated with a requested set of forces and moments to be applied to the aircraft is received. An optimal mix of actuators and associated actuator parameters to achieve to an extent practical the requested forces and moments is computed, including by taking into consideration dynamically varying effectiveness of one or more actuators based on a current dynamic state of the aircraft. An output comprising for each actuator in the optimal mix a corresponding set of one or more control signals associated with the set of actuator parameters computed for that actuator is provided.

CROSS REFERENCE TO OTHER APPLICATIONS

This application is a continuation of co-pending U.S. patent applicationSer. No. 15/593,819, entitled GEOMETRY-BASED FLIGHT CONTROL SYSTEM filedMay 12, 2017 which is incorporated herein by reference for all purposes.

BACKGROUND OF THE INVENTION

Flight control systems (sometimes referred to as “flight controllers”)translate inputs received from manually operated pilot controls, such asone or more of a roll stick, rudder pedals, pitch stick, collective, andthrottle controls, etc., referred to collectively herein as “inceptors”,and/or inputs from one or more sensors (e.g., air speed) into commandsto the flight control assets of the aircraft, which may include one ormore of control surfaces, such as rudders, ailerons, elevators, etc.;sources of forward thrust, such as propellers or jet engines; poweredsources of lift such as rotors or lift fans; and forces capable of beingdirected or otherwise controlled or concentrated through use of nozzles,diverters, physical structures onto which engine or fan thrust may bedirected, such as vanes, etc. and/or rotation of thrust generatingdevices.

Each of the foregoing, and/or other equipment and structures, may beused to affect one or more of the speed, direction, andattitude/orientation of the aircraft, and depending on the input eachmay be able to contribute to satisfying a command/input received via oneor more inceptors. Aerodynamic control surfaces, powered sources ofcontrol forces, such as lift fans, and other structures capable ofaffecting aircraft attitude, motion, and/or orientation are referred tocollectively herein as “actuators”.

An aircraft typically is considered to have six degrees of freedom ofmovement, including forces in the forward/back, side/side, and up/downdirections (corresponding to forces in three axes Fx, Fy, and Fz) andmoments about the longitudinal (roll) axis, the transverse (pitch) axis,and the vertical (yaw) axis (corresponding to moments in three axes Mx,My, and Mz). If an aircraft has more actuators than degrees of freedom,it must be determined how the various actuators will be used to act onthe aircraft in response to commands received via the inceptors orsensors. For a given set of one or more pilot commands under givencircumstances, some combinations of actuators capable of acting on theaircraft to achieve the result indicated by the pilot command(s) may bemore effective and/or efficient than others. For example, some mayconsume more or less power and/or fuel than others, provide a moresmooth transition from a current state than others, etc. Consideredcircumstances may include the current position of the aircraft. In theevent of an aircraft with dynamic geometry, the position of an aircraftcomponent (e.g. a tiltwing) may be considered in determining theactuator commands. Constraints such as evenly distributed thrusts oractuator limitations may be considered in determining the actuatorcommands. Even for systems that are not over-actuated, it is necessaryto determine what combination of actuation commands results in thedesired motion and how to manage saturation issues or enforce systemconstraints.

In prior systems, attempts have been made to pre-compute, offline,combinations of actuators and associated parameters (e.g., position,speed, other parameters) to respond to different combinations ofinceptor input using heuristics and/or engineering judgement. However,typically it is not possible to determine in advance every combinationof actuators and associated parameters that may be required under allpossible conditions and circumstances.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments of the invention are disclosed in the followingdetailed description and the accompanying drawings.

FIG. 1 is a diagram illustrating an embodiment of a geometry-basedflight control system.

FIG. 2 is a flow diagram illustrating an embodiment of a geometry-basedflight control process.

FIG. 3A is a diagram illustrating an embodiment of a tiltwing aircraft.

FIG. 3B is a diagram illustrating an embodiment of a tiltwing aircraft.

FIG. 4A is a diagram illustrating an embodiment of a tiltwing.

FIG. 4B is a diagram illustrating an embodiment of a tiltwing.

FIG. 5 is a diagram illustrating an embodiment of an optimizationproblem controller.

FIG. 6 is a flow diagram illustrating an embodiment of a geometry-basedflight control process.

FIG. 7 is a diagram illustrating an embodiment of a moment vectorsolver.

FIG. 8 is a diagram illustrating an embodiment of a rotor geometrymatrix solver.

FIG. 9 is a diagram illustrating an embodiment of a control surfacegeometry matrix solver.

FIG. 10 is a diagram illustrating an embodiment of system controls.

FIG. 11 is a diagram illustrating an embodiment of a process toconfigure a geometry-based flight control system.

FIG. 12 is a diagram illustrating an embodiment of a geometry-basedflight control system.

FIG. 13 is a flowchart illustrating an embodiment of a geometry-basedflight control process.

FIG. 14 is a flowchart illustrating an embodiment of a geometry-basedflight control process.

FIG. 15 is a flowchart illustrating an embodiment of a geometry-basedflight control process.

DETAILED DESCRIPTION

The invention can be implemented in numerous ways, including as aprocess; an apparatus; a system; a composition of matter; a computerprogram product embodied on a computer readable storage medium; and/or aprocessor, such as a processor configured to execute instructions storedon and/or provided by a memory coupled to the processor. In thisspecification, these implementations, or any other form that theinvention may take, may be referred to as techniques. In general, theorder of the steps of disclosed processes may be altered within thescope of the invention. Unless stated otherwise, a component such as aprocessor or a memory described as being configured to perform a taskmay be implemented as a general component that is temporarily configuredto perform the task at a given time or a specific component that ismanufactured to perform the task. As used herein, the term ‘processor’refers to one or more devices, circuits, and/or processing coresconfigured to process data, such as computer program instructions.

A detailed description of one or more embodiments of the invention isprovided below along with accompanying figures that illustrate theprinciples of the invention. The invention is described in connectionwith such embodiments, but the invention is not limited to anyembodiment. The scope of the invention is limited only by the claims andthe invention encompasses numerous alternatives, modifications andequivalents. Numerous specific details are set forth in the followingdescription in order to provide a thorough understanding of theinvention. These details are provided for the purpose of example and theinvention may be practiced according to the claims without some or allof these specific details. For the purpose of clarity, technicalmaterial that is known in the technical fields related to the inventionhas not been described in detail so that the invention is notunnecessarily obscured.

A geometry-based flight control system is disclosed. The geometry-basedflight control system receives a set of inputs associated with arequested set of forces and moments to be applied to the aircraft andcomputes an optimal mix of actuators and associated actuator parametersto achieve to an extent practical the requested forces and moments.Computing the optimal mix of actuators and associated actuatorparameters includes taking into consideration dynamically varyingeffectiveness of one or more actuators based on a dynamic state of theaircraft. For example, the dynamic state may comprise an angle of atiltwing of the aircraft or an angle of a tilt rotor of the aircraft.

In some embodiments, the flight control system allows a user to input anaircraft's geometry information and desired flight trajectory andreceive a set of actuator commands that achieve the desired flighttrajectory. The flight control system may be portable to various typesof aircraft. Aircraft geometry information may comprise the shape andphysical attributes of the aircraft. The flight control system accountsfor dynamic aircraft geometry. For example, an aircraft with tiltwingshas different aircraft geometry at different points in flight based onthe tilt angle of the wings. The flight control system may determineactuator commands based on the real-time tilt angle of the wings. Insome embodiments, the geometry-based flight control system providesreal-time, online optimization. The system may provide optimizedactuator commands that allow the aircraft to utilize its fullcapabilities.

FIG. 1 is a diagram illustrating an embodiment of a geometry-basedflight control system. In the example shown, geometry-based flightcontrol system 100 comprises optimization problem controller 104 andsystem controls 106. Sensors 101, inceptors 102, and system controls 106provide inputs to optimization problem controller 104. Optimizationproblem controller 104 outputs actuator commands to actuators 108.

Sensors 101 may comprise an accelerometer, thermometer, barometer,gyroscope, radar, sonar, or any appropriate sensor. The sensors may bepositioned on the aircraft and provide sensor data relevant to saidaircraft. Sensors 101 may provide information on the aircraft's currentstate and environment to optimization problem controller 104.Information on the aircraft's current state may comprise airspeed,attitude, position, wing tilt angles, control surface tilt angles, orany other appropriate information. Inceptors 102 may comprise aircraftcontrols such as a joystick, trigger, lever, electronic interface, orany other appropriate apparatus. Inceptors 102 may be controlled by apilot in the aircraft or on ground. Inceptors 102 may provideinformation to optimization problem controller 104 regarding a desiredcourse of flight or action for the aircraft.

Optimization problem controller 104 may comprise an interface andprocessor. Optimization problem controller 104 may determine, based atleast in part on the sensor data and inceptor information, an optimalset of actuator commands that achieve the desired flight trajectory. Theoptimization problem controller may comprise a control loop thatdetermines a set of forces and moments required to achieve the flighttrajectory as instructed via the inceptors. The optimization problemcontroller may further determine geometry information regarding theaircraft. Geometry/performance matrices containing information onexpected impact or effects of actuators of the aircraft based on thegeometry (e.g. current positioning of the aircraft and aircraftcomponents) of the aircraft may be determined. The geometry matrices maybe affected by non-geometric dynamic factors as well, such as airspeed.The geometry matrices may be calculated based on the current state ofthe aircraft, accounting for changing states. The effect of the variousactuators included in an aircraft on forces and/or moments along and/orabout the respective axes of the aircraft (e.g., x, y, and z;longitudinal, transverse, and vertical; etc.) may be determined based onactuator attributes and aircraft geometry. In some embodiments, actuatoreffectiveness under different operating conditions (e.g., airspeed,temperature, etc.) may be taken into consideration. Based on therequired forces and moments and geometry information, the optimizationproblem controller may determine actuator parameters that achieve(within an acceptable margin of error) the required moments and forces.The parameters may be provided in the form of commands to the actuators.

System controls 106 may comprise variables, constraints, or otherfactors that affect the resulting actuator commands. A pilot or otherrelevant body may use the system controls to tweak the flight controlsystem while the aircraft in is flight or grounded. The system controlsmay be used to penalize utilizing a certain actuator, establish maximumand minimums on the commanded actuator parameters, or otherwise modifythe optimization performed by the optimization problem controller.

FIG. 2 is a flow diagram illustrating an embodiment of a geometry-basedflight control process. At 200, inceptor and/or sensor inputs arereceived. A desired flight trajectory and current aircraft positioninginformation are received. At 202, required forces and moments aredetermined. Dynamic aircraft geometry information is also determined.The required forces and moments may be determined based on the desiredflight trajectory. The dynamic aircraft geometry information maycomprise models, matrices, or other data regarding the aircraft'sposition and the effect on actuators. At 206, actuator commands aredetermined. The actuator commands may be provided to the actuators.Subsequent iterations are repeated until the flight control process isdone (208), e.g., the aircraft lands and is shut down.

FIG. 3A is a diagram illustrating an embodiment of a tiltwing aircraft.In some embodiments, the geometry-based flight control system is used ina tiltwing aircraft. In various embodiments, the system may be used in ahelicopter, multicopter, or any appropriate vehicle. In the exampleshown, aircraft 300 comprises wings 304 and 308. The wings arepositioned parallel with the fuselage of the aircraft, e.g. in anon-tilted state. Multiple rotors may be present on each wing. As shown,rotor 302 is attached to the front of wing 304 and rotor 306 is attachedto the front of wing 308. Each wing may comprise two rotors, one on eachside of the fuselage of the aircraft. Aircraft 300 further comprisestailpiece 310 and wheels 312 and 314.

FIG. 3B is a diagram illustrating an embodiment of a tiltwing aircraft.As shown, wings 304 and 308 are tilted upwards away from the nose of theaircraft. The angle of front wing 304 is shown as θ₁ and the angle ofback wing 308 is shown as θ₂. In some embodiments, the wings are tiltedat the same angle. In some embodiments, the wings are tilted atdifferent angles. In some embodiments, the aircraft as shown isover-actuated. Multiple combinations of actuators and actuatorparameters may achieve an ordered flight trajectory. The describedflight control system may determine an optimal combination of actuatorsand actuator parameters.

FIG. 4A is a diagram illustrating an embodiment of a tiltwing. Tiltwingsmay comprise control surfaces, for example flaps or panels. In theexample shown, wing 400 is tilted at an angle of θ₁. Two rotors areshown on wing 400. T₁ and T₂ as shown refer to the respective thrusts ofthe rotors. The individual thrusts of rotors of the aircraft are solvedfor. Panels 402 and 404 are shown on wing 400. Panels 402 and 404 may berepositioned during flight to change the flight trajectory. For example,adjusting the angle of the control surfaces may impact lift orefficiency of flight. The panels may be controlled by servomechanisms orelectric linear actuators.

FIG. 4B is a diagram illustrating an embodiment of a tiltwing. In theexample shown, panels 402 and 404 are angled away from the body of wing400. Panel 402 is positioned at an angle of from θ₁ the wing. Panel 404is positioned at an angle of δ₂ from the wing. The static geometry ofthe aircraft as well as the dynamic geometry of the aircraft, forexample, the angle of the tilt wings and control surfaces, may be usedto determine the optimal actuator commands. As part of thegeometry-based flight control process, the impact of the dynamiccomponents of the aircraft may be determined each time new flighttrajectory instructions are received.

FIG. 5 is a diagram illustrating an embodiment of an optimizationproblem controller. The optimization problem controller may determine anoptimization problem based on the aircraft information, wherein solvingthe optimization problem determines optimal actuator instructions. Theoptimization problem may comprise the sum of moment error, thrust error,a rotor regularization term, and a control surface regularization term,over which thrusts and control surface angles are minimized. Theoptimization problem may be structured as shown below:

$\min\limits_{T,\delta}( {{\frac{1}{2}{{M - ( {{AT} + {B\;\delta} + c} )}}_{W_{M}}^{2}} + {( {T_{desired} - {\sum\limits_{i}T_{i}}} )^{2}w_{T}} + {ɛ_{T}{\sum\limits_{i}( \frac{T_{i} - T_{\min_{i}}}{T_{\max_{i}} - T_{\min_{i}}} )^{2}}} + {ɛ_{\delta}{\sum\limits_{i}( \frac{\delta_{i} - \delta_{0_{i}}}{\delta_{\max_{i}} - \delta_{\min_{i}}} )^{2}}}} )$     subject  to  T_(min_(i)) ≤ T_(i) ≤ T_(max_(i))In which,

M is a moment vector;

A is a rotor geometry matrix;

T is a rotor thrust vector;

B is a control surface geometry matrix;

δ is a control surface tilt angle vector;

c is an offset vector;

W_(M) is a moment error weight;

W_(T) is a thrust error weight;

ε_(T) is a rotor regularization error; and

ε_(δ) is a control surface regularization error.

Moment error is represented by M−(AT+Bδ+c). Moment vector M may comprisethe required moments and average thrust required for the aircraft toachieve a desired flight trajectory. Moment vector M may comprisemoments in three axes Mx, My, Mz and a thrust. In the example shown,moment vector solver 502 receives inputs from inceptors 501 and sensors505 and outputs the moment vector.

A geometry matrix as described is equivalent to a performance matrix.For example, the matrix contains information pertaining to performanceof an actuator based at least in part on the aircraft's geometry. Rotorgeometry matrix A may comprise information on the effect of rotors ofthe aircraft as a function of aircraft geometry. The rotor geometrymatrix may be a function of wing tilt angles θ₁ and θ₂ in a tilt wingaircraft. In the example shown, rotor geometry matrix solver 504receives inputs from sensors 505 and outputs the rotor geometry matrix.Control surface geometry matrix B may comprise information on the effectof control surfaces of the aircraft as a function of aircraft geometry.The control surface geometry matrix may be a function of wing tiltangles θ₁ and θ₂ in a tiltwing aircraft. In the example shown, controlsurface geometry matrix solver 506 receives inputs from sensors 505 andoutputs the control surface geometry matrix. Optimization problemcontroller 500 comprises moment vector solver 502, rotor geometry matrixsolver 504, and control surface geometry matrix solver 506.

Rotor thrust vector T may comprise thrusts of each individual rotor ofthe aircraft. Each individual rotor thrust may be constrained withinshared specified upper and lower bounds(T_(lower bound)≤T≤T_(upper bound)) or each rotor may have differingmaximum and minimum thrusts. Control surface vector δ may compriseangles of each individual control surface of the aircraft. Rotor thrustvector T and control surface vector δ may be solved for afterdetermination of moment vector M, rotor geometry matrix A, and controlsurface geometry matrix B such that T and δ are minimized over theoptimization problem. Offset vector c may be used to ensure accuracy ofthe equation. For example, a flap may provide non-zero lift when it isinline with a wing so an offset correctly calibrates the zero liftposition. Thrust error is represented by T_(desired)−Σ_(i)T_(i). The sumof thrusts of all rotors is subtracted from the desired thrust.

Rotor regularization term

$\sum\limits_{i}( \frac{T_{i} - T_{\min_{i}}}{T_{\max_{i}} - T_{\min_{i}}} )^{2}$penalizes having a large spread of rotor utilization. It optimizes forthe rotors being roughly similarly utilized, e.g. all rotors engagedbetween 30% to 40% rather than one rotor utilized at 100% of itspossible thrust and other rotors barely utilized. The term accounts forrotors of different sizes by summing the percentage of thrust used foreach rotor. The maximum and minimum thrusts of each rotor areconsidered. In the event all rotors of the aircraft are identical, theterm optimizes for a small distribution of thrusts. Similarly, controlsurface regularization term

$\sum\limits_{i}( \frac{\delta_{i} - \delta_{0_{i}}}{\delta_{\max_{i}} - \delta_{\min_{i}}} )^{2}$penalizes having a large spread of control surface utilization andaccounts for different configurations (e.g. different sizes) of controlsurfaces. It optimizes for the control surfaces being roughly similarlyutilized in proportion to their ranges. In the event all controlsurfaces of the aircraft are identical, the term optimizes for a smalldistribution of control surface angles. Term δ₀ _(i) refers to adeflection point or trim condition. It describes the angle of thecontrol surface during cruise.

The weights and errors may be used to tune the optimization problem. Forexample, W_(M) may comprise a vector with weights for roll, pitch, andyaw. W_(M) and W_(T) may be adjusted such that roll and pitch areweighted over thrust and yaw is weighted the least. The weights may beused to prioritize stability. For example, roll and pitch may be weighedmore because they are critical to the aircraft's stability and thrust isnot. Errors ε_(δ) and ε_(T) may be used to change the priority of thecontrol surface angle distribution and the thrust distribution.

FIG. 6 is a flow diagram illustrating an embodiment of a geometry-basedflight control process. At 600, inceptor and/or sensor inputs arereceived. The inceptor and/or sensor inputs may contain a desired flighttrajectory or action. At 602, a moment vector is determined. The momentvector may comprise the required moments and average thrust needed toachieve the desired flight trajectory. At 604, a rotor geometry matrixand control surface geometry matrix are determined. The geometrymatrices may be determined based on static and dynamic geometry aspectsof the aircraft. In some embodiments, models are determined for therotors and control surfaces and the models are linearized. The rotorgeometry matrix and control surface geometry matrix result fromlinearizing the models. At 606, rotor and control surface parameters aresolved for. They may be solved for by solving an optimization problembased upon the moment vector, rotor geometry matrix, and control surfacegeometry matrix. At 608, rotor and control surface commands areprovided. The aircraft actuators may comprise rotors and controlsurfaces. In the event that an aircraft comprises other actuators,various relevant geometry matrices and parameters may be determined.Subsequent iterations are repeated until the flight control process isdone (610), e.g., the aircraft lands and is shut down.

FIG. 7 is a diagram illustrating an embodiment of a moment vectorsolver. The moment vector solver may receive a desired flight trajectoryand determine the moments and average thrust required to achieve thedesired flight trajectory. As shown, moment vector solver 700 outputs amoment vector. In the example shown, inceptors 702 provide desiredaircraft behavior to moment vector solver 700. The desired aircraftbehavior may comprise aircraft parameters such as a change in yaw,acceleration, or another flight metric. A pilot or remote operator maymanipulate inceptors of the aircraft, instructing a desired flighttrajectory or desired action for the aircraft. For example, a pilot mayturn a steering wheel left by a few degrees, indicating a desired yaw.The moment vector solver may apply a control law to its inputs todetermine a moment vector. The moment vector solver may determine theappropriate moments and forces to achieve the desired aircraftparameters based on the aircraft's current state. In the example shown,a current state of the aircraft is received from sensors 704 andestimator 706. In some embodiments, the current state of the aircraft isdetermined based on sensor data. For example, readings from instrumentson the aircraft may determine the tilt of the wings, the speed of theaircraft, and any other appropriate aircraft information. In someembodiments, an estimator provides an estimate of the current aircraftstate based on a history of past actuator commands. For example, a modelof tiltwing angles may be utilized in addition past commands in order toestimate the current tilt angle.

In some embodiments, the desired flight trajectory may be determinedbased on sensor data. For example, after an initial destination input,the aircraft may be flown using autopilot. In some embodiments, inceptorinputs may be overridden based on sensor data. For example, in the eventsensor data shows an obstacle in a trajectory the pilot has directed theaircraft, a moment vector may be determined that directs the aircraft ina different route.

FIG. 8 is a diagram illustrating an embodiment of a rotor geometrymatrix solver. In the example shown, rotor geometry matrix solverreceives sensor data from sensors 802. The sensor data comprises theaircraft front wing tilt angle, the aircraft back wing tilt angle, theaircraft front wing position, the aircraft back wing position, theaircraft airspeed, and the rotor untilted positions among other metrics.In various embodiments, various types of sensor data are used. Forexample, a rotor tilt angle would be considered for a tilt rotoraircraft whereas a wing tilt angle would not be used. Based on thesensor data, the rotor geometry matrix solver outputs a rotor geometrymatrix.

The rotor geometry matrix solver may first determine a model for rotorbehavior. In some embodiments, the model is determined applying theequation r×T_(i){circumflex over (t)} to each rotor of the aircraft.Thrust offset r describes the rotor location compared to a center ofgravity of the aircraft. Thrust direction {circumflex over (t)}describes the directionality of the rotor's thrust. Rotor thrust T_(i)is the rotor's thrust (to be solved for). The thrust offset and thrustdirection may comprise spatial rotation information. The model maychange based on changes in the aircraft's state. For example, a new wingtilt angle, new rotor tilt angle, or new airspeed may change the model.Linearizing the model for one rotor may provide one row of the rotorgeometry matrix. The process may be repeated for each rotor until therotor geometry matrix is complete.

FIG. 9 is a diagram illustrating an embodiment of a control surfacegeometry matrix solver. In the example shown, sensors 902 provide sensordata to control surface geometry matrix solver 900, which outputs acontrol surface geometry matrix. The sensor data for a tiltwing aircraftcomprises aircraft wing tilt angles and aircraft airspeed. Airspeed maybe measured using a pitot tube or sensor. In some embodiments, theairspeed is interpolated based on the wing tilt angle of the aircraft.Sensors may be inaccurate in determining airspeed. The airspeed may beestimated based on a known relationship between wing tilt angle andairspeed. In the example shown, control surface geometry matrix solvercomprises linear interpolator 904, which receives aircraft wing tiltangles.

In some embodiments, past commands are used to determine the aircraft'scurrent position and parameters rather than sensor data. Past actuatorcommands may be analyzed to determine a current state of the aircraft.In some embodiments, sensor data is used estimate or determine specificaircraft parameters or position data that cannot be captured viasensors. For example, wind speed sensors may be unreliable. Globalpositioning system (GPS) data may be used to determine wind speed.

The control surface geometry matrix solver may first determine a modelfor control surface behavior and linearize the model to solve for thematrix. In some embodiments, the model is determined applying theequation r×∂_(i){circumflex over (t)} to each control surface of theaircraft. Lift offset r describes the control surface location comparedto a center of gravity of the aircraft. Lift direction {circumflex over(t)} describes the directionality of the lift caused by the controlsurface deflection. Control surface angle ∂_(i) is the control surfaceangle (to be solved for). The lift offset and lift direction maycomprise spatial rotation information. The model may change based onchanges in the aircraft's state. For example, a new wing tilt angle, newrotor tilt angle, or new airspeed may change the model. Under highairspeeds, a small deflection in a control surface has a larger impacton aircraft flight as compared low airspeeds. Linearizing the model forone control surface may provide one row of the control surface geometrymatrix. The process may be repeated for each rotor until the controlsurface geometry matrix is complete.

FIG. 10 is a diagram illustrating an embodiment of system controls. Inthe example shown, system controls 1000 comprise weight and errorcontrols 1002, mode switch 1004, and sensor/estimator switch 1006. Thesystem controls may be used to specify restrictions or considerationsfor the aircraft's flight trajectory. The system controls may alter theoptimization problem and affect the determined actuator commands. Thesystem controls may be embodied in a graphic user interface. In someembodiments, the system controls may be changed while in the aircraft ismid-flight. In some embodiments, the system controls are changed basedon sensor data or information regarding the aircraft's current state(e.g. attitude, position, airspeed, tilt angles). The system controlsmay be remotely changed, changed by a pilot, or changed by autopilotsoftware.

Error controls 1002 may be used to change error and weight values in theoptimization problem, such as values ε_(δ), ε_(T), W_(T), and W_(M). Theerror and weight values may be manipulated to establish a hierarchy ofprioritization of roll, pitch, yaw, and thrust. The values may be set inorder to artificially constrain the aircraft. Artificially constrainingthe aircraft may diminish the number of possible solutions to theoptimization problem, allowing it to be solved faster. Artificiallyconstraining the aircraft may provide buffer room in case the aircraftexperiences an emergency and needs extra power.

The error controls may be used to establish minimum and maximum rotorthrust values. The error controls may also be used to determine adistribution of rotor thrusts. For example T_(mean)−ε₂≤T≤T_(mean)+ε₂ maybe included in the optimization problem, wherein ε₂ is an error valueused to ensure the ordered rotor thrusts are roughly evenly distributed.Providing a roughly even distribution of rotor thrusts may spread theload or torque evenly among the rotors and also prevent the optimizationproblem from becoming a nonconvex program that has multiple solutions.

Mode switch 1004 may comprise various modes of flight. The mode switchmay comprises various modes or settings of autopilot. Differentpreferences of flight selected via the mode switch may change the momentvector. For example, a smooth ride mode may result in differing actuatorcommands than a sport ride mode.

Sensor/estimator switch 1006 may be used to determine whether thegeometry-based flight control system relies on aircraft state/positioninformation from sensor data or from an estimator that analyses pastactuator commands to determine an estimate of the aircraftstate/position.

FIG. 11 is a diagram illustrating an embodiment of a process toconfigure a geometry-based flight control system. At 1100, constraints,weights, and errors are determined. At 1102, the system is configured toperform an optimization with respect to the constraints, weights, anderrors to select in response to a set of forces and moments acorresponding optimal mix of actuator commands. The constraints,weights, and errors may be used to fine tune the optimization problem toachieve the desired actuator commands under the pertinent circumstances.

FIG. 12 is a diagram illustrating an embodiment of a geometry-basedflight control system. In the example shown, geometry-based flightcontrol system 1200 comprises estimator 1204, optimization problemcontroller 1208, quadratic program solver 1210, and inceptors 1212.Sensors 1202 provide aircraft wing tilt information to optimizationproblem controller 1208. Based on the aircraft wing tilt information andinceptor commands received from inceptors 1212, the optimization problemcontroller determines an optimization problem. Rotor geometry matrix Aand control surface geometry matrix B may be non-linear models orfunctions that are programmed to model the actuator response based onthe aircraft's geometry. The optimization problem controller maycomprise software that linearizes the functions or models into matrices.The models may be linearized based on the current positioning of dynamiccomponents of the aircraft (e.g. wing tilt angle). Followinglinearization based on the dynamic state, the optimization problem maybe solved using quadratic problem solver 1210. The optimization problemmay be formatted as a linear problem that is able to be solved using aquadratic problem solver. The quadratic problem solver as shown providesactuator commands to actuators 1202. Actuator commands may also beprovided to estimator 1204, which determines an estimate of the currentaircraft state based on past commands. Optimization problem controller1208 may utilize aircraft wing tilt information and other aircraft stateinformation based on estimator outputs, sensor outputs, or both.

FIG. 13 is a flowchart illustrating an embodiment of a geometry-basedflight control process. An iteration of the process may be performed atpredetermined time intervals or each time new inceptor or autopilotinputs are received. For example, the system may run at 100 Hz. At 1300,pilot commands and aircraft state information are received. At 1302, anoptimization problem is determined. The optimization problem maycomprise non-linear models. In some embodiments, a moment vector isdetermined anew in each iteration based on inputted inceptor commands orautopilot commands. Models for the rotors and control surfaces may bedetermined anew in each iteration based on the aircraft's current state.The models may be functions of the aircraft's dynamic geometry. At 1304,the optimization problem is linearized based on aircraft stateinformation. For example, the optimization problem may be linearizedbased on the wing tilt angle(s) of the aircraft and many other factors.Linearizing the optimization problem may comprise linearizing non-linearmodels (e.g. rotor or control surface models) into matrices. At 1306,the optimization problem is solved. At 1308, it is determined whetherflight is complete. Subsequent iterations are repeated until it isdetermined that flight is complete.

FIG. 14 is a flowchart illustrating an embodiment of a geometry-basedflight control process. At 1400, aircraft state information and inceptorinputs are received. At 1402, optimization problem matrices and vectorsare determined. At 1404, it is determined whether weights or errors havebeen changed. In the event a change has occurred, at 1406 the weight orerror change(s) are inserted. Following weight and error changedetermination and possible insertion, at 1410 it is determined whethersensor mode is indicated. In the event sensor mode is indicated, at 1412sensor aircraft state information is utilized. In the event sensor modeis not indicated, at 1414 estimator aircraft state information isutilized. Mode switch determinations may also be considered. Following1412 or 1414, at 1416 the optimization problem is linearized based onaircraft state information. At 1418, the optimization problem is solvedusing a quadratic program solver. At 1420, commands are provided toactuators. At 122, it is determined whether flight is complete.Subsequent iterations are repeated until it is determined that flight iscomplete.

FIG. 15 is a flowchart illustrating an embodiment of a geometry-basedflight control process. At 1500, actuator availability, health, andeffectiveness are monitored under current conditions. For example, aspeed controller for a rotor may report to the geometry-based flightcontrol system in the event the rotor is not working. At 1502, asolution space based on available actuators and current capacity of eachis determined. For example, a broken rotor may be entered as a zero inthe determined optimization problem. Detection of a broken actuator maytrigger automatic changes in the optimization problem controller. Theoptimization continues while using the remaining actuators. At 1504,within the solution space an optimal mix of actuator commands thatsatisfies one or more constraints is found.

Although the foregoing embodiments have been described in some detailfor purposes of clarity of understanding, the invention is not limitedto the details provided. There are many alternative ways of implementingthe invention. The disclosed embodiments are illustrative and notrestrictive.

What is claimed is:
 1. A method of controlling flight of an aircraft,comprising: receiving a set of inceptor inputs associated with arequested set of forces and moments to be applied to the aircraft;computing an optimal mix of actuators and associated actuator parametersto achieve to an extent practical the requested forces and moments,including by taking into consideration dynamically varying effectivenessof one or more actuators based on a current dynamic state of theaircraft, the dynamic state including aircraft wing tilt angle, whereinthe optimal mix of actuators includes one or more rotors and for eachrotor taking into consideration dynamically varying effectivenessincludes using a moment vector, a control surface performance matrix,and a dynamically updated rotor geometry matrix to determine the effectof said one or more rotors under various combinations of actuators andassociated parameters given the current dynamic state of the aircraft,wherein the computing of the optimal mix of actuators and the associatedactuator parameter includes: computing the moment vector based on theset of inceptor inputs and a current state of the aircraft, comprising:determining the current state of the aircraft, wherein the current stateof the aircraft is determined based on sensor data and an estimate ofthe current state of the aircraft, the sensor data including a wing tiltangle of the aircraft, wherein the estimate of the current state of theaircraft is determined based on past actuator commands, wherein the wingtilt angle of the aircraft includes a first wing tilt angle and a secondwing tilt angle, wherein the first wing tilt angle relates to an angleof a front wing, and wherein the second wing tilt angle relates to anangle of a back wing; computing the control surface performance matrixbased on the first wing tilt angle and the second wing tilt angle; andcomputing the dynamically updated rotor performance matrix based on thefirst wing tilt angle and the second wing tilt angle; and providing anoutput comprising for each actuator in the optimal mix a correspondingset of one or more control signals associated with the actuatorparameters computed for that actuator.
 2. The method of claim 1, whereinthe optimal mix is computed at least in part by formulating anassociated optimization problem as a quadratic program.
 3. The method ofclaim 1, wherein the optimal mix is computed at least in part byformulating an associated optimization problem comprising a rotorperformance matrix, control surface performance matrix, and therequested set of forces and moments to be applied to the aircraft. 4.The method of claim 1, wherein computing the optimal mix includesdetermining a set of thrusts for rotors of the aircraft and a set ofangles for control surfaces of the aircraft.
 5. The method of claim 1,wherein the non-linear model for actuator performance is determinedbased on spatial rotation information.
 6. The method of claim 1, whereinthe optimal mix is computed at least in part by determining a model forcontrol surface performance based on the dynamic state of the aircraft.7. The method of claim 1, wherein computing the optimal mix includesoptimizing for an equal utilization of all rotors relative to theirmaximum and minimum thrusts.
 8. The method of claim 1, wherein computingthe optimal mix includes optimizing for an equal utilization of allcontrol surfaces relative to their maximum and minimum possible angles.9. The method of claim 1, wherein computing the optimal mix includesdetermining weights to prioritize roll, pitch, yaw, and thrusts inrelation to each other.
 10. The method of claim 1, wherein computing theoptimal mix includes monitoring actuator health and adjusting anassociated optimization problem accordingly.
 11. The method of claim 1,wherein the wing tilt angle of the aircraft is determined based on thepast actuator commands.
 12. An aircraft flight control system,comprising: an interface configured to receive: a set of inceptor inputsassociated with a requested set of forces and moments to be applied tothe aircraft; and a set of sensor inputs reflecting a current dynamicstate of the aircraft, the dynamic state including aircraft airspeed andwing tilt angle; and a processor coupled to the interface and configuredto: compute an optimal mix of actuators and associated actuatorparameters to achieve to an extent practical the requested forces andmoments, including by taking into consideration dynamically varyingeffectiveness of one or more actuators based on the current dynamicstate of the aircraft, wherein the optimal mix of actuators includes oneor more rotors and for each rotor taking into consideration dynamicallyvarying effectiveness includes using a moment vector, a control surfaceperformance matrix, and a dynamically updated rotor performance matrixto determine the effect of said one or more rotors under variouscombinations of actuators and associated parameters given the currentdynamic state of the aircraft, wherein the computing of the optimal mixof actuators and the associated actuator parameter includes to: computethe moment vector based on the set of inceptor inputs and a currentstate of the aircraft, comprising to: determine the current state of theaircraft, wherein the current state of the aircraft is determined basedon sensor data and an estimate of the current state of the aircraft, thesensor data including the wing tilt angle of the aircraft, wherein theestimate of the current state of the aircraft is determined based onpast actuator commands, wherein the wing tilt angle of the aircraftincludes a first wing tilt angle and a second wing tilt angle, whereinthe first wing tilt angle relates to an angle of a front wing, andwherein the second wing tilt angle relates to an angle of a back wing;compute the control surface performance matrix based on the first wingtilt angle and the second wing tilt angle; and compute the dynamicallyupdated rotor performance matrix based on the first wing tilt angle andthe second wing tilt angle; and provide an output comprising for eachactuator in the optimal mix a corresponding set of one or more controlsignals associated with the actuator parameters computed for thatactuator.
 13. The system of claim 12, wherein the optimal mix iscomputed at least in part by formulating an associated optimizationproblem as a quadratic program.
 14. The system of claim 12, wherein theoptimal mix is computed at least in part by formulating an associatedoptimization problem comprising the rotor performance matrix, thecontrol surface performance matrix, and the requested set of forces andmoments to be applied to the aircraft.
 15. The system of claim 12,wherein computing the optimal mix includes determining a set of thrustsfor rotors of the aircraft and a set of angles for control surfaces ofthe aircraft.
 16. The system of claim 12, wherein the non-linear modelfor actuator performance is determined based on spatial rotationinformation.
 17. A computer program product to control flight of anaircraft, the computer program product being embodied in anon-transitory computer readable medium and comprising computerinstructions for: receiving a set of inceptor inputs associated with arequested set of forces and moments to be applied to the aircraft;computing an optimal mix of actuators and associated actuator parametersto achieve to an extent practical the requested forces and moments,including by taking into consideration dynamically varying effectivenessof one or more actuators based on a current dynamic state of theaircraft, the dynamic state including aircraft wing tilt angle, whereinthe optimal mix of actuators includes one or more rotors and for eachrotor taking into consideration dynamically varying effectivenessincludes using a moment vector, a control surface performance matrix,and a dynamically updated rotor geometry performance matrix to determinethe effect of said one or more rotors under various combinations ofactuators and associated parameters given the current dynamic state ofthe aircraft, wherein the computing of the optimal mix of actuators andthe associated actuator parameter includes: computing the moment vectorbased on the set of inceptor inputs and a current state of the aircraft,comprising: determining the current state of the aircraft, wherein thecurrent state of the aircraft is determined based on sensor data and anestimate of the current state of the aircraft, the sensor data includinga wing tilt angle of the aircraft, wherein the estimate of the currentstate of the aircraft is determined based on past actuator commands,wherein the wing tilt angle of the aircraft includes a first wing tiltangle and a second wing tilt angle, wherein the first wing tilt anglerelates to an angle of a front wing, and wherein the second wing tiltangle relates to an angle of a back wing; computing the control surfaceperformance matrix based on the first wing tilt angle and the secondwing tilt angle; and computing the dynamically updated rotor geometryperformance matrix based on the first wing tilt angle and the secondwing tilt angle; and providing an output comprising for each actuator inthe optimal mix a corresponding set of one or more control signalsassociated with the actuator parameters computed for that actuator.